CRM Saturday London 2017

Two sessions presented.

 

Sales effectiveness in Dynamics CRM with the help of Azure IoT and Machine Learning

How can an organisation optimise its sales channels and product targeting by building a 365-degree view of its customers in Dynamics CRM? The answer, and topic of this session, is with the help of Azure IoT and Machine Learning services!

The use case described is the identification of common patterns of actions by consumers, classification based on criteria like age, gender, location, etc., and promotion of best-fit products and services. To achieve this objective, wearable and mobile devices are used and connected to the Azure IoT Hub for collecting information about location, commuting patterns and weather condition. All this information is then scored and evaluated in Azure Machine Learning to predict the best matching products and services. Data about sales conversion and customer loyalty is also captured and analysed with the Azure HDInsight platform, and displayed via Power BI.

Targeted at software architects, developers and product owners, this session focusses on presenting all the technologies used to build the discussed use case and how to integrate them in an end-to-end fully functional solution.

 

 

Ranking CRM leads with Azure Machine Learning

Every day, EF Education First receives thousands of expressions of interest by prospective students to attend an education program delivered in any of the 150+ locations around the world. How is all this information processed promptly in order to provide a swift and effective response to applicants? We rank leads and interests based on program, location, past history, and hundreds, literally, of other criteria. We cannot do this manually clearly. We use the power of outcome prediction algorithms in Azure Machine Learning.

Targeted at software architects, developers and product owners, this session explores the foundation of Azure Machine Learning for building outcome prediction services, describing how data is collected and defined into a model; how the model is trained and then scored; and finally how the evaluation of the model is processed to generate the ranked outcome.

Custom decision-tree algorithms are presented in the programming language R, along with RESTful Web Services consumed by our CRM application. This session completes also with the illustration of best practices and guidelines for maintaining and deploying large-scale datasets in the Cloud and optimisation of computing time of ML experiments.


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